Atmospheric phenomena, such as haze (including fog, smoke, smog, drizzle and other particulate matter) can degrade images and videos by obscuring objects, decreasing contrast, and decreasing color fidelity. Haze may be caused by the particles in the air or particles in the water for underwater video. These particles may attenuate the light reflected from objects in the scene of an image. These particles may also scatter ambient light (sometimes known as “airlight”) toward the imaging system that takes the images/videos. The degradation of image quality makes it more difficult to identify objects and targets in the images and videos.
Prior art methods have attempted to improve image quality based on atmospheric phenomena such as haze. One such method involves a polarizing filter. This method has drawbacks in that two orthogonally polarized images must be acquired to create a single de-hazed image. Moreover, this method requires that the scene be static, thus limiting the usefulness of the method for real-time video.
Another method requires capturing multiple images with the same scene under different atmospheric conditions. However, this method is not suitable for cameras on moving platforms. Yet another method requires a reference model to estimate the amount of haze, which is used to iteratively or recursively de-haze the image. However, generating a reference model is impractical when the scene is unknown a priori, and further, iterative processing on images can be prohibitively slow.
More recently, the dark channel prior method has received much attention. This method allows single image de-hazing by estimating the transmission (depth) map of objects in the scene. The dark channel prior method de-hazes based on the principle that the amount of scattering is directly proportional to the distance of the objects from the camera. Scattering is typically not uniform across an image since objects in the image are at different distances.
Many variations of the dark channel prior method may be found in the literature. One disadvantage of these dark channel prior-type methods is that they typically require a complex and time-consuming refinement of the transmission map. This refinement typically involves soft-matting, anisotropic diffusion, or bilateral filtering, all of which are computationally intensive. Moreover, many of these methods work on stock photography, but not in practical video applications where the scene or illumination changes quickly.
Furthermore, many of the prior art methods produce images that look unnatural due to inaccurate color restoration. These images may also look unnatural because they have halos around the edges of objects. These methods can also blur the original scene, so while the haze might be reduced, the sharpness of the original image is also degraded.
There is thus a need for a system and method for haze removal and/or reduction that addresses the shortcomings of the prior art. For example, there is a need for a system and method for haze removal and/or reduction that can be used for real-time video applications where the scene or illumination changes quickly, and that is less computationally intensive.